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According to recent analyst research, many companies see master data management (MDM) as a cure for integration or data management mistakes. The issue with approaching MDM in that way is the risk of missing the forest for all the trees. While many individuals naively consider MDM to be a technology issue, others consider the way that MDM processes and policies can address, and ultimately “fix”, both data and integration challenges.

The way we perceive our approach to MDM is that it is not about technology, and in fact, is not really about the data either. Yet this concept is often difficult for people to grasp, especially folks in IT. But the bottom line is clear: MDM is about using key data to improve the business, and if the program is not approached in that way, it increases the risk of failure.

If you step back from your MDM program and examine why it’s important and how it can enrich your business, you’ll see that every aspect of a successful MDM initiative revolves around the concept of business improvement. For example, you can’t strengthen customer relationships if you don’t know who your customers are. It’s unwise to create and produce innovative products without a way to efficiently get the products in the hands of your customers. The examples go on and on. Data is at the heart of business. And good data is the engine that drives successful businesses.

We have identified three areas of business problems that good data can help combat: risk mitigation, cost control and revenue optimization. Using data to see the big picture – whether it’s due diligence in an acquisition or assuring regulatory compliance reporting – can greatly reduce risk exposure. Data can also be used to control costs. Properly managed data can help companies unearth the tiny areas where money is leaking out of the organization – ways that could never be tracked manually. And, with a diligent data quality approach, you can deliver significant revenue gains for your business.

Still not convinced MDM is a business issue? Consider the fact that businesses have thousands of business processes to execute each day. A recent Forrester Research study estimated only 5 to 15 percent of those processes are automated, which means the overwhelming majority of companies rely on human involvement to execute these processes. MDM-as-technology apologists would argue this only shows the white space that exists in getting more data automatically processed. Our approach is that MDM creates an environment where companies can automate those business processes, get consistent execution on them and optimize them based on factual data that comes from the organization. It’s more than just automating data; it’s doing something useful with it that drives business and increases profits.

Look at how data can be stored in an organization – in independent silos. Let’s say marketing is using a database to generate leads for sales. Finance has another database with customer history and corporate P&L. Sales and manufacturing have yet another database to manage order history and product inventory. A successful campaign by the marketing team is deemed a success because the featured product’s sales are up. However, the financial reality is that those sales came at the expense of another product’s sales, so it wasn’t really a success. Meanwhile, manufacturing didn’t know about the campaign, so it didn’t adjust inventory levels. The company must now deal with an inventory surplus, customer dissatisfaction because orders aren’t being shipped and a financial hit to the bottom line. All because data isn’t being shared.

It’s true that part of a successful MDM initiative involves cleaning up and sorting through this data that has traditionally been kept in silos. However, it isn’t meant to cause finger-pointing within an organization. After all, it was just the way things were done. And while cleaning up databases and correcting the problems of the past is part of effective MDM, it shouldn’t be the main driver of the program.

The bottom line is this: If the goal of your MDM program is just to have immaculate data, you’re missing the point. The data must be driving a business goal. Otherwise, the return on your MDM investment will never materialize, because your business process won’t improve. In today’s competitive environments, with the customer, employee and regulatory demands – it is essential to run your business as efficiently as possible. The key to better business is better data, managing and funding your data infrastructure like you would your other corporate assets. This is only achievable if you build data management and data governance processes based on business requirements.

Too often, data governance teams rely on existing measurements as the metrics used to populate a data quality scorecard. But without a defined understanding of the relationship between specific measurement scores and the business’s success criteria, it is difficult to determine how to react to emergent data quality issues - and determine whether their fixing these problems has any measurable business value. This white paper by David Loshin explores ways to qualify data control and measures to support the governance program.

Operational data governance is the manifestation of the processes and protocols necessary to ensure that an acceptable level of confidence in the data effectively satisfies the organization’s business needs. In this white paper, David Loshin from Knowledge Integrity examines how a data governance program defines the roles, responsibilities, and accountabilities associated with managing data quality, and how a data quality scorecard provides an effective management tool for monitoring organizational performance with respect to data quality control.